mindspore.ops.Concat

class mindspore.ops.Concat(*args, **kwargs)[source]

Connect tensor in the specified axis.

Connect input tensors along with the given axis.

The input data is a tuple of tensors. These tensors have the same rank R. Set the given axis as m, and \(0 \le m < R\). Set the number of input tensors as N. For the \(i\)-th tensor \(t_i\), it has the shape of \((x_1, x_2, ..., x_{mi}, ..., x_R)\). \(x_{mi}\) is the \(m\)-th dimension of the \(i\)-th tensor. Then, the shape of the output tensor is

\[(x_1, x_2, ..., \sum_{i=1}^Nx_{mi}, ..., x_R)\]

Parameters: axis (int) – The specified axis. Default: 0.

Inputs:

input_x (tuple, list) - A tuple or a list of input tensors. input_x, input_y should has same data type.
input_y (tuple, list) - A tuple or a list of input tensors. input_x, input_y should has same data type.

Outputs:

Tensor, the shape is \((x_1, x_2, ..., \sum_{i=1}^Nx_{mi}, ..., x_R)\).: The data type is the same with input_X and input_y.

Raises: TypeError – If axis is not an int.

Supported Platforms:: Ascend GPU CPU

Examples

>>> input_x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.float32))
>>> input_y = Tensor(np.array([[0, 1], [2, 1]]).astype(np.float32))
>>> op = ops.Concat()
>>> output = op((input_x, input_y))
>>> print(output)
[[0. 1.]
 [2. 1.]
 [0. 1.]
 [2. 1.]]
>>> op = ops.Concat(1)
>>> output = op((input_x, input_y))
>>> print(output)
[[0. 1. 0. 1.]
 [2. 1. 2. 1.]]